Get all the packages ready

Load data

# Load Medland survey collection data
medland_survey2014_2017 <- read_csv("data/medland_survey2014_2017.csv", locale = locale(encoding = "850"))
Rows: 1501 Columns: 28── Column specification ───────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (5): study.area, sector, subsector, visibility, time.date
dbl (23): ID, zone, collection, xcoord, ycoord, area.sqm, undiag.lithics, burins, notch.dent, MP.to...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Figures 1 and 2 generated in GIS software

Figure 3: Surface Visibility and Artifact Recovery

medland_survey2014_2017 %>% 
  dplyr::filter(area.sqm>0 & total.lithics>0 & !is.na(visibility)) %>% 
  mutate(lithic.density=total.lithics/area.sqm) %>% 
  ggplot(aes(x=lithic.density), xlim=.02) + 
  geom_histogram(binwidth = .001) + 
  scale_y_log10() + 
  scale_x_continuous(limits = c(0,0.01), breaks = c(0,.002, .004, .006, .008)) + 
  labs(title = "Surface Visibility and Artifact Recovery",
       x="lithic artifacts / km^2",
       y='count of patches') + 
  facet_grid(factor(visibility)~study.area) + 
  theme_bw(base_size = 20) + 
  theme(axis.title.x = element_markdown())

ANOVA for Figure 3: all survey areas and each survey area

cat("\nCanal de Navarrés survey area\n")

Canal de Navarrés survey area
with(medland_survey2014_2017 %>% 
       dplyr::filter(total.lithics>0 & !is.na(visibility) & study.area == "Canal de Navarrés") %>% 
       mutate(lithic.density=total.lithics/area.sqm), 
  aov(lithic.density~visibility)) %>% summary()
             Df    Sum Sq   Mean Sq F value Pr(>F)
visibility    2 0.0000022 1.104e-06   0.451  0.637
Residuals   194 0.0004746 2.446e-06               
cat("\nHoya de Buñol survey area\n")

Hoya de Buñol survey area
with(medland_survey2014_2017 %>% 
       dplyr::filter(total.lithics>0 & !is.na(visibility) & study.area == "Hoya de Buñol") %>% 
       mutate(lithic.density=total.lithics/area.sqm), 
  aov(lithic.density~visibility)) %>% summary()
            Df    Sum Sq   Mean Sq F value Pr(>F)
visibility   2 1.220e-06 6.095e-07     0.4  0.671
Residuals   98 1.493e-04 1.523e-06               
cat("\nCocina/Catadau survey area\n")

Cocina/Catadau survey area
with(medland_survey2014_2017 %>% 
       dplyr::filter(total.lithics>0 & !is.na(visibility) & study.area == "Cocina/Catadau") %>% 
       mutate(lithic.density=total.lithics/area.sqm), 
  aov(lithic.density~visibility)) %>% summary()
            Df    Sum Sq   Mean Sq F value Pr(>F)  
visibility   2 0.0001894 9.470e-05   2.662 0.0758 .
Residuals   83 0.0029531 3.558e-05                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Random Forest Model for Chronological Unmixing (Figure 4 and Table 4)

Data preparation

Prepare data for Valencia dated assemblages



# Modify training data based on ML and Bayesian testing: 
#  Merge ENEOL and MNEOL
#  Remove undiagnostic lithics for age estimates
#  Sort factor levels chronologically 

training_data_E_Iberia <- training_data_E_Iberia %>% 
  select(-undiag.lithics, -total.lithics, -citation) %>% 
  mutate(period = replace(period, period == "MNEOL", "ENEOL"), 
         period = factor(period,
                         levels = c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")))

Prepare data from survey collections for applying Random Forest model

# filter out assemblages with only undiagnostic lithics
medland_survey2014_2017_lithics <- medland_survey2014_2017 %>% 
  dplyr::filter(undiag.lithics < total.lithics) %>% 
  select(ID, c(13:27))

# make separate table of assemblage ID and provenience
medland_survey2014_2017_info <- medland_survey2014_2017 %>% 
  dplyr::filter(undiag.lithics < total.lithics) %>% 
  mutate(assemblage = paste(study.area, "-", zone, "-", sector, "-", subsector, sep = "")) %>% 
  select(ID, study.area, assemblage)

# calculate lithic density for each collection patch
medland_survey2014_2017_density <- medland_survey2014_2017 %>% 
  dplyr::filter(undiag.lithics < total.lithics) %>% 
  mutate(assemblage = paste(study.area, "-", zone, "-", sector, "-", subsector, sep = ""), 
         density.km2 = total.lithics*1000/area.sqm)%>% 
  select(ID, total.lithics, area.sqm,density.km2)

Split data into training and test sets

# Partition into training and hold out test / validation sample
set.seed(456) ## if we want to make it completely reproducible
vl.split <- training_data_E_Iberia %>% 
  rsample::initial_split(., prop=.75)

vl.train <- rsample::training(vl.split)
vl.test <- rsample::testing(vl.split)

# save ID data for later analysis
vl.test.id <- vl.test %>% 
  select(ID, period)

create v-fold objects for replicable and comparable cross-validation

10 folds using all the training data

set.seed(456)
vf10.all <- vfold_cv(training_data_E_Iberia %>% select(-ID),v=10)

10 folds using the 75% training data split

set.seed(456)
vf10.train <- vfold_cv(vl.train %>% select(-ID),v=10)

Test Random Forest Model for Estimating Age of Surface Assemblages

Create and evaluate RF model using a 75% split (vl.split)

Define and instantiate a random forest model

valencia.lithics.rf.mod <- 
  rand_forest(trees=500) %>% 
  set_engine("ranger", importance = "impurity") %>% 
  set_mode("classification")

print(valencia.lithics.rf.mod)
Random Forest Model Specification (classification)

Main Arguments:
  trees = 500

Engine-Specific Arguments:
  importance = impurity

Computational engine: ranger 

Fit the model to the training data

valencia.lithics.rf.fit <- 
  valencia.lithics.rf.mod %>% 
  fit(as.factor(period) ~ ., data = vl.train[,2:ncol(vl.train)])

print(valencia.lithics.rf.fit)
parsnip model object

Ranger result

Call:
 ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~500,      importance = ~"impurity", num.threads = 1, verbose = FALSE,      seed = sample.int(10^5, 1), probability = TRUE) 

Type:                             Probability estimation 
Number of trees:                  500 
Sample size:                      96 
Number of independent variables:  15 
Mtry:                             3 
Target node size:                 10 
Variable importance mode:         impurity 
Splitrule:                        gini 
OOB prediction error (Brier s.):  0.2416893 

Optional graph: variable importance for random forest model with training set

valencia.lithics.rf.fit %>% 
  extract_fit_engine() %>% 
  vip(aesthetics = list(color = "black", fill = "#26ACB5"), num_features = 15) + theme_minimal()

Extract the fitted data

valencia.lithics.rf.predicted <- 
  valencia.lithics.rf.fit %>% 
  predict(vl.test) %>% 
  bind_cols(vl.test.id[1:2], ., predict(valencia.lithics.rf.fit, vl.test, type="prob")) %>% 
  rename(predicted.age = .pred_class, 
         MP=.pred_MP,
         UP=.pred_UP,
         EPI=.pred_EPI, 
         MESO=.pred_MESO, 
         ENEOL=.pred_ENEOL, 
         LNEOL=.pred_LNEOL, 
         true.age = period) %>% 
  mutate(true.age = factor(true.age, 
           levels=c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")),
         predicted.age = factor(predicted.age, 
           levels=c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")))

print(valencia.lithics.rf.predicted)

Figure 4: Graph random forest predictions for test set

valencia.lithics.rf.predicted %>% 
  pivot_longer(cols = 4:ncol(valencia.lithics.rf.predicted), 
               names_to = "period", values_to = "probability") %>% 
  mutate(period = factor(period, 
      levels = c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL"))) %>%
  ggplot(aes(x=period, y=probability)) + 
  geom_line(group=1) + 
  geom_vline(aes(xintercept = true.age), color="red", size=2, alpha=.5) +
  geom_vline(aes(xintercept = predicted.age), color="blue", size=0.8) + 
  labs(title="Random Forest Predictions for Each Assemblage", 
       subtitle="black line indicates probability, blue line indicates predicted age, & red line indicates known age",
       x="time period",
       y="probability of predicted time period") + 
  facet_wrap(vars(ID), ncol = 7) + 
  theme_bw(base_size = 20) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        strip.text.x = element_text(size = 14))

Create confusion matrix for 75% training/test split

library(caret)
with(valencia.lithics.rf.predicted,  
  caret::confusionMatrix(true.age, predicted.age))
Confusion Matrix and Statistics

          Reference
Prediction MP UP EPI MESO ENEOL LNEOL
     MP    13  0   0    0     0     0
     UP     0  2   0    0     2     0
     EPI    0  1   1    1     0     0
     MESO   0  0   0    3     1     0
     ENEOL  0  0   0    1     5     0
     LNEOL  0  0   0    0     2     1

Overall Statistics
                                          
               Accuracy : 0.7576          
                 95% CI : (0.5774, 0.8891)
    No Information Rate : 0.3939          
    P-Value [Acc > NIR] : 2.407e-05       
                                          
                  Kappa : 0.6788          
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: MP Class: UP Class: EPI Class: MESO Class: ENEOL Class: LNEOL
Sensitivity             1.0000   0.66667    1.00000     0.60000       0.5000      1.00000
Specificity             1.0000   0.93333    0.93750     0.96429       0.9565      0.93750
Pos Pred Value          1.0000   0.50000    0.33333     0.75000       0.8333      0.33333
Neg Pred Value          1.0000   0.96552    1.00000     0.93103       0.8148      1.00000
Prevalence              0.3939   0.09091    0.03030     0.15152       0.3030      0.03030
Detection Rate          0.3939   0.06061    0.03030     0.09091       0.1515      0.03030
Detection Prevalence    0.3939   0.12121    0.09091     0.12121       0.1818      0.09091
Balanced Accuracy       1.0000   0.80000    0.96875     0.78214       0.7283      0.96875

Create and evaluate cross-validated random forest model using 10 folds

Define and instantiate a random forest model workflow and fit to cross-validated data set

#Create workflow step
valencia.lithics.rf.wf <- 
  workflow() %>% 
  add_model(valencia.lithics.rf.mod) %>% 
  add_formula(period ~ .) #The predictor is contained in add_formula method

set.seed(456) # For reproducibility
valencia.lithics.rf.xv.fit <- 
  valencia.lithics.rf.wf %>% 
  fit_resamples(vf10.all,
                control=control_resamples(save_pred = TRUE))
! Fold01: internal: No observations were detected in `truth` for level(s): 'UP', 'LNEOL'
Computation will...
! Fold02: internal: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL'
Computation wil...
! Fold03: internal: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL'
Computation wil...
! Fold04: internal: No observations were detected in `truth` for level(s): 'UP'
Computation will proceed ...
! Fold06: internal: No observations were detected in `truth` for level(s): 'EPI', 'MESO'
Computation will...
! Fold09: internal: No observations were detected in `truth` for level(s): 'LNEOL'
Computation will proce...
! Fold10: internal: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL'
Computation wil...
print(valencia.lithics.rf.xv.fit)
# Resampling results
# 10-fold cross-validation 

There were issues with some computations:

  - Warning(s) x3: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL' Computation wi...   - Warning(s) x1: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL' Computation wi...   - Warning(s) x1: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL' Computation wi...   - Warning(s) x1: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL' Computation wi...   - Warning(s) x1: No observations were detected in `truth` for level(s): 'EPI', 'LNEOL' Computation wi...

Use `collect_notes(object)` for more information.

Collect the cross-validated metrics

# Collect the metrics using another model with cross-validation
valencia.lithics.rf.xv.meanpreds <- tune::collect_metrics(valencia.lithics.rf.xv.fit)
print(valencia.lithics.rf.xv.meanpreds)

Optional Graph: variable importance for cross-validated random forest model

valencia.lithics.rf.mod %>% 
  fit(as.factor(period) ~ ., data = training_data_E_Iberia[,2:ncol(training_data_E_Iberia)]) %>% 
  extract_fit_engine() %>% 
  vip(aesthetics = list(color = "black", fill = "#26ACB5"), num_features = 15) + 
  theme_minimal()

Extract predictions of random forest model with cross-validation

valencia.lithics.rf.xv.predictions <- collect_predictions(valencia.lithics.rf.xv.fit, summarize = TRUE) %>% 
  arrange(.row) %>% 
  rename(predicted.age = .pred_class,
         MP=.pred_MP,
         UP=.pred_UP,
         EPI=.pred_EPI, 
         MESO=.pred_MESO, 
         ENEOL=.pred_ENEOL, 
         LNEOL=.pred_LNEOL,
         true.age=period) %>% 
  select(-.row, -.config) %>% 
  relocate(predicted.age, .after = true.age) %>% 
  bind_cols(training_data_E_Iberia[1], .)

print(valencia.lithics.rf.xv.predictions)

Optional Graph: results for cross-validated random forest predictions

valencia.lithics.rf.xv.predictions %>% 
  pivot_longer(cols = 4:ncol(valencia.lithics.rf.xv.predictions), names_to = "period", values_to = "probability") %>% 
  mutate(period = factor(period, levels = c("MP","UP","EPI","MESO","ENEOL","MNEOL","LNEOL"))) %>% 
ggplot(aes(x=period, y=probability)) + 
  geom_line(group=1) + 
  geom_vline(aes(xintercept = true.age), color="red", size=2, alpha=.5) +
  geom_vline(aes(xintercept = predicted.age), color="blue", size=0.8) + 
  labs(title="Random Forest with Cross-Validated Predictions", 
       subtitle="Time Periods for Each Assemblage",
       x="predicted time period\n(blue line indicates prediction & red line indicates radiocarbon age)") +
  facet_wrap(vars(ID)) + 
  theme_bw(base_size = 16) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Table 4: Confusion matrix for cross-validated random forest predictions

caret::confusionMatrix(
  as.factor(valencia.lithics.rf.xv.predictions$true.age),
  as.factor(valencia.lithics.rf.xv.predictions$predicted.age))
Confusion Matrix and Statistics

          Reference
Prediction MP UP EPI MESO ENEOL LNEOL
     MP    51  0   0    0     0     0
     UP     0  9   1    0     5     0
     EPI    1  3   2    0     0     0
     MESO   0  3   0   11     5     0
     ENEOL  0  1   0    6    22     0
     LNEOL  0  0   0    0     3     6

Overall Statistics
                                          
               Accuracy : 0.7829          
                 95% CI : (0.7018, 0.8507)
    No Information Rate : 0.4031          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.7073          
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: MP Class: UP Class: EPI Class: MESO Class: ENEOL Class: LNEOL
Sensitivity             0.9808   0.56250    0.66667     0.64706       0.6286      1.00000
Specificity             1.0000   0.94690    0.96825     0.92857       0.9255      0.97561
Pos Pred Value          1.0000   0.60000    0.33333     0.57895       0.7586      0.66667
Neg Pred Value          0.9872   0.93860    0.99187     0.94545       0.8700      1.00000
Prevalence              0.4031   0.12403    0.02326     0.13178       0.2713      0.04651
Detection Rate          0.3953   0.06977    0.01550     0.08527       0.1705      0.04651
Detection Prevalence    0.3953   0.11628    0.04651     0.14729       0.2248      0.06977
Balanced Accuracy       0.9904   0.75470    0.81746     0.78782       0.7771      0.98780

Create random forest model from all training data to estimate ages of surface collections from survey

Define and instantiate a Random Forest model using all the known training data

medland.survey.rf.mod <- 
  rand_forest(trees=500) %>% 
  set_engine("ranger") %>% 
  set_mode("classification")

print(medland.survey.rf.mod)
Random Forest Model Specification (classification)

Main Arguments:
  trees = 500

Computational engine: ranger 

Extract fit of Random Forest model

medland.survey.rf.fit <- 
  medland.survey.rf.mod %>% 
  fit(as.factor(period) ~ ., data = training_data_E_Iberia[,2:ncol(training_data_E_Iberia)])

print(medland.survey.rf.fit)
parsnip model object

Ranger result

Call:
 ranger::ranger(x = maybe_data_frame(x), y = y, num.trees = ~500,      num.threads = 1, verbose = FALSE, seed = sample.int(10^5,          1), probability = TRUE) 

Type:                             Probability estimation 
Number of trees:                  500 
Sample size:                      129 
Number of independent variables:  15 
Mtry:                             3 
Target node size:                 10 
Variable importance mode:         none 
Splitrule:                        gini 
OOB prediction error (Brier s.):  0.2341059 

Apply Random Forest model to surface collections and generate age estimate predictions for each collection

medland.survey.rf.predicted <- 
  medland.survey.rf.fit %>% 
  predict(medland_survey2014_2017_lithics) %>% 
  bind_cols(medland_survey2014_2017_info, ., 
            predict(medland.survey.rf.fit, medland_survey2014_2017_lithics, type="prob")) %>% 
  rename(predicted.period = .pred_class, 
         MP=.pred_MP,
         UP=.pred_UP,
         EPI=.pred_EPI, 
         MESO=.pred_MESO, 
         ENEOL=.pred_ENEOL, 
         LNEOL=.pred_LNEOL) %>% 
  mutate(predicted.period = factor(predicted.period, 
           levels=c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")))

print(medland.survey.rf.predicted)

Calculate age probabilities to assign to collections with only undiagostic lithics

Use the 10th percentile of probabilities from random forest model with dated collections

valencia.lithics.rf.probquantiles <- with(valencia.lithics.rf.xv.predictions %>% 
       pivot_longer(cols = 4:ncol(valencia.lithics.rf.xv.predictions), 
                    values_to = "probability"), 
       quantile(probability, probs = c(.1, .25, .5, .75))) 

print(paste("10th percentile of random forest probabilities =", 
            valencia.lithics.rf.probquantiles[1]))
[1] "10th percentile of random forest probabilities = 0.00173557343664358"

Optional Graph: show quantiles for random forest probabilities

# show this in a graph
valencia.lithics.rf.xv.predictions %>% 
  pivot_longer(cols = 4:ncol(valencia.lithics.rf.xv.predictions), names_to = "period", values_to = "probability") %>% 
  mutate(period = factor(period, levels = c("MP","UP","EPI","MESO","ENEOL","MNEOL","LNEOL"))) %>% 
  ggplot(aes(x=probability)) + 
  geom_density() + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[1], color = 'red') + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[2], color = 'green') + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[3], color = 'blue') + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[4], color = 'green') +
  labs(title="Distribution of Combined Random Forest Predictions", 
       x="age prediction probabilities \n(blue = median, red = 10th percentile, green = 25th and 75th percentile")

Create files for additional graphing and for output for mapping

# Create base file for graphing and output
medland.survey.rf.graph <-  
  left_join(select(medland_survey2014_2017, ID, study.area, zone, sector, subsector, total.lithics, area.sqm) %>% 
            mutate(assemblage = paste(study.area, "-", zone, "-", sector, "-", subsector, sep = "")), 
            medland.survey.rf.predicted) %>% 
  dplyr::filter(total.lithics>0) %>% 
  mutate(density.km2 = total.lithics/area.sqm/1000)
Joining, by = c("ID", "study.area", "assemblage")
# Create output file
# Use 10th percentile for overall ubiquity for patches with only undiagnostic lithics
medland.survey.rf.out <- medland.survey.rf.graph %>% 
  mutate(MP = replace_na(MP, valencia.lithics.rf.probquantiles[1]), 
         UP = replace_na(UP, valencia.lithics.rf.probquantiles[1]), 
         EPI = replace_na(EPI, valencia.lithics.rf.probquantiles[1]), 
         MESO = replace_na(MESO, valencia.lithics.rf.probquantiles[1]), 
         ENEOL = replace_na(ENEOL, valencia.lithics.rf.probquantiles[1]), 
         LNEOL = replace_na(LNEOL, valencia.lithics.rf.probquantiles[1])) %>% 
  # occupational ubiquity
  rename(MP_ubiq = MP, 
         UP_ubiq = UP, 
         EPI_ubiq = EPI, 
         MESO_ubiq = MESO,
         ENEOL_ubiq = ENEOL, 
         LNEOL_ubiq = LNEOL) %>% 
  # calculate occupational intensity
  mutate(MP_int = MP_ubiq*density.km2/800, 
         UP_int = UP_ubiq*density.km2/250, 
         EPI_int =  EPI_ubiq*density.km2/40, 
         MESO_int = MESO_ubiq*density.km2/35, 
         ENEOL_int = ENEOL_ubiq*density.km2/25, 
         LNEOL_int = LNEOL_ubiq*density.km2/12)

# create file to graph results of patches with diagnostic lithics
medland.survey.rf.graph <-  medland.survey.rf.graph %>% 
  dplyr::filter(!is.na(predicted.period)) %>% 
  pivot_longer(cols = 10:15, 
               names_to = "period", 
               values_to = "ubiquity") %>% 
   mutate(period = factor(period, 
                         levels = c("MP","UP","EPI","MESO","ENEOL","MNEOL","LNEOL")),
         age = case_when(period == "MP" ~ 80000, 
                         period == "UP" ~ 25000, 
                         period == "EPI" ~ 13000, 
                         period == "MESO" ~ 9000, 
                         period == "ENEOL" ~ 6000,
                         period == "LNEOL" ~ 4000), 
         predicted.age = case_when(
                         predicted.period == "MP" ~ 80000, 
                         predicted.period == "UP" ~ 25000, 
                         predicted.period == "EPI" ~ 13000, 
                         predicted.period == "MESO" ~ 9000, 
                         predicted.period == "ENEOL" ~ 6000,
                         predicted.period == "LNEOL" ~ 4000), 
         duration.centuries = case_when(
                         period == "MP" ~ 800, 
                         period == "UP" ~ 250, 
                         period == "EPI" ~ 40, 
                         period == "MESO" ~ 35, 
                         period == "ENEOL" ~ 25,
                         period == "LNEOL" ~ 12))

Optional Graph: occupational ubiquity for all survey patches with diagnostic lithics

medland.survey.rf.graph %>% 
  ggplot(aes(x=period, y=ubiquity)) + 
  geom_rect(aes(fill = study.area),xmin = -Inf,xmax = Inf, ymin = -Inf,ymax = Inf,alpha = 0.1) +
  geom_line(group=1) + 
  #geom_vline(aes(xintercept = predicted.period), color="blue") + 
  labs(title="Random Forest Age Estmates for Medland Survey Data", 
       subtitle="Time Periods for Each Assemblage, Colored by Study Area",
       x="predicted time period\n(blue line indicates maximum predicted probability)") +
  facet_wrap(~ assemblage + ID) + 
  theme_bw(base_size = 16) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Optional Graph: land use intensity for all survey patches with diagnostic lithics

medland.survey.rf.graph %>% 
  ggplot(aes(x=period, y=ubiquity*density.km2/duration.centuries)) + 
  geom_rect(aes(fill = study.area),xmin = -Inf,xmax = Inf, ymin = -Inf,ymax = Inf,alpha = 0.1) +
  geom_line(group=1) + 
  #geom_vline(aes(xintercept = predicted.period), color="blue") + 
  labs(title="Land Use Intensity Estmates for Medland Survey Data", 
       subtitle="Time Periods for Each Assemblage, Colored by Study Area",
       x="predicted time period\n(blue line indicates maximum predicted probability)",
       y="estimated artifact accumulation rate\n(artifacts/km2/century)") +
  facet_wrap(~ assemblage + ID) + 
  theme_bw(base_size = 16) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Figure 6a

medland.survey.rf.graph %>% 
  dplyr::filter(ID==238 | ID==960) %>% 
ggplot(aes(x=period, y=ubiquity)) + 
  geom_line(group=1, size=2) + 
  labs(title="Occupational Ubiquity for 2 Survey Patches", 
       subtitle="Random Forest Age Estmates",
       x="predicted time period", 
       y="occupational ubiquity") +
  facet_wrap(~ assemblage) + 
  theme_bw(base_size = 20) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Figure 6b

medland.survey.rf.graph %>% 
  dplyr::filter(ID==238 | ID==960) %>% 
ggplot(aes(x=period, y=ubiquity*density.km2/duration.centuries)) + 
  geom_line(group=1, size=2) + 
  labs(title="Land Use Intensity for 2 Survey Patches", 
       subtitle="Random Forest Age Estmates",
       x="predicted time period", 
       y="artifacts / km^2 /century") +
  facet_wrap(~ assemblage) + 
  theme_bw(base_size = 20) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        axis.title.y = element_markdown()) 

Save random forest dating file to csv for mappoing in GIS

write_csv(medland.survey.rf.out, "medland_survey_rf.csv")

Aggregate summaries of ubiquity and intensity for each valley

Calcuate area surveyed totals for each valley

totals.surveyed <- medland_survey2014_2017 %>% 
  select(study.area, area.sqm) %>% 
  group_by(study.area) %>% 
  summarise(total.area.sqm=sum(area.sqm))

Figure 10a: aggregate ubiquity for each valley

left_join(medland.survey.rf.graph, totals.surveyed) %>% 
  dplyr::filter(ubiquity > quantile(medland.survey.rf.graph$ubiquity, 
                                     probs = .5)) %>% 
  group_by(study.area, period) %>% 
  summarize(area.sum = sum(area.sqm), 
            total.area.sqm = first(total.area.sqm)) %>% 
  select(study.area, period, area.sum, total.area.sqm) %>% 
  mutate(pct.coverage = area.sum/total.area.sqm) %>% 
  ungroup()  %>% 
  add_row(study.area = "Canal de Navarrés",
          period = "EPI",
          area.sum = 0,
          total.area.sqm = 4107582,
          pct.coverage = 0) %>%
  add_row(study.area = "Cocina/Catadau",
          period = "EPI",
          area.sum = 0,
          total.area.sqm = 1095683,
          pct.coverage = 0) %>%
  add_row(study.area = "Cocina/Catadau",
          period = "MESO",
          area.sum = 0,
          total.area.sqm = 1095683,
          pct.coverage = 0) %>%
  add_row(study.area = "Hoya de Buñol",
          period = "EPI",
          area.sum = 0,
          total.area.sqm = 1551377,
          pct.coverage = 0) %>% 

# Needed for upper quartile graphing   
  # add_row(study.area = "Canal de Navarrés",
  #         period = "MP",
  #         area.sum = 0,
  #         total.area.sqm = 4107582,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Canal de Navarrés",
  #         period = "MESO",
  #         area.sum = 0,
  #         total.area.sqm = 4107582,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Canal de Navarrés",
  #         period = "LNEOL",
  #         area.sum = 0,
  #         total.area.sqm = 4107582,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Cocina/Catadau",
  #         period = "MP",
  #         area.sum = 0,
  #         total.area.sqm = 1095683,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Cocina/Catadau",
  #         period = "LNEOL",
  #         area.sum = 0,
  #         total.area.sqm = 1095683,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Hoya de Buñol",
  #         period = "MP",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
  # add_row(study.area = "Hoya de Buñol",
  #         period = "MESO",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
  # add_row(study.area = "Hoya de Buñol",
  #         period = "LNEOL",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
  
    
  ggplot(aes(x=factor(period, levels = 
                  c('MP','UP','EPI','MESO','ENEOL','LNEOL')),
             y=pct.coverage, group=1)) + 
  geom_line(lwd=1.5) + 
  facet_wrap(~study.area, ncol = 1) + 
  labs(title='Aggregate Occupational Ubiquity', 
       subtitle='Proportion of study area surveyed\nwith ubiquity above the median', 
       x='period', 
       y='proportion') + 
  theme_bw(base_size = 16)
Joining, by = "study.area"`summarise()` has grouped output by 'study.area'. You can override using the `.groups` argument.

Figure 10b: aggregate intensity for each valley

left_join(medland.survey.rf.graph, totals.surveyed) %>% 
  mutate(intensity = ubiquity*density.km2/duration.centuries) %>% 
  dplyr::filter(intensity >= quantile(medland.survey.rf.graph$ubiquity*medland.survey.rf.graph$density.km2/medland.survey.rf.graph$duration.centuries, probs =.5)) %>% 
  group_by(study.area, period) %>% 
  summarize(area.sum = sum(area.sqm), 
            total.area.sqm = first(total.area.sqm), 
            density.km2 = first(density.km2)) %>% 
  select(study.area, period, area.sum, total.area.sqm) %>% 
  mutate(pct.coverage = area.sum/total.area.sqm) %>% 
  ungroup %>%
  add_row(study.area = "Canal de Navarrés",
          period = "MP",
          area.sum = 0,
          total.area.sqm = 4107582,
          pct.coverage = 0) %>% 
  add_row(study.area = "Hoya de Buñol",
          period = "MP",
          area.sum = 0,
          total.area.sqm = 1551377,
          pct.coverage = 0) %>% 
    
# Needed for optional upper quartile graphing
  # add_row(study.area = "Hoya de Buñol",
  #         period = "UP",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
      
  ggplot(aes(x=factor(period, levels = 
                  c('MP','UP','EPI','MESO','ENEOL','LNEOL')),
             y=pct.coverage, group=1)) + 
  geom_line(lwd=1.5) + 
  facet_wrap(~study.area, ncol = 1) + 
  labs(title='Aggregate Land Use Intensity', 
       subtitle='Proportion of study area surveyed\nwith intensity above the median', 
       x='period', 
       y='proportion') + 
  theme_bw(base_size = 16)
Joining, by = "study.area"`summarise()` has grouped output by 'study.area'. You can override using the `.groups` argument.

Figure 11: SPD Analyses of Prehistoric Demography

Prepare Data

Only use dates with COV ≤ 0.05

C14_SE_Iberia_all <- C14_SE_Iberia_all %>% dplyr::filter(C14.CV<0.05 & C14.SD>0)

Calibrate Dates with BChron

all.dates.calibrated <- with(C14_SE_Iberia_all, BchronCalibrate(ages = C14.mean, ageSds = C14.SD, calCurves = calib.curve, positions = site))

C14_SE_Iberia_all$BP.cal.median <- sapply(1:length(all.dates.calibrated), function(x) round(median(all.dates.calibrated[[x]]$ageGrid)))

Bin Dates

C14_SE_Iberia_all.bins <- C14_SE_Iberia_all %>% 
  with(., binPrep(site, C14.mean, 100))

Model Test

C14_SE_Iberia_all.modeltest <- C14_SE_Iberia_all %>% 
  with(., calibrate(x=C14.mean, errors=C14.SD, calCurves = calib.curve, normalised=TRUE, calMatrix=FALSE)) %>% 
  modelTest(., 
            errors = C14_SE_Iberia_all$C14.SD, 
            timeRange = c(35000,3000), 
            runm = 500, 
            model="exponential", 
            datenormalised=TRUE, 
            nsim = 200, 
            ncores = ncores,
            method = 'calsample', 
            bins = C14_SE_Iberia_all.bins)
[1] "Calibrating radiocarbon ages..."

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[1] "Done."
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1


Attaching package: ‘snow’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    parCapply, parLapply, parRapply, parSapply, splitIndices,
    stopCluster
[1] "Aggregating observed dates..."
[1] "Monte-Carlo test..."

Plot SPD

par(mar=c(7,7,7,3))
plot(C14_SE_Iberia_all.modeltest, xlim = c(30000,4000), col.obs = 'black', lwd.obs = 5, drawaxes = F)
axis(1, cex.axis = 2, pos = -.01, at=(seq(30000,0, by=-5000)))
axis(2, cex.axis = 2, pos = 30200)
mtext(side=1, line=5, "calibrated years BP", cex=2.5)
mtext(side=2, line=4, "summed probability density", cex=2.5)
title(main=paste("Southern and Eastern Iberia SPD (N = ", nrow(C14_SE_Iberia_all), ")\n"), cex.main = 3)

---
title: "A Multi-method Approach with Machine Learning to Evaluating the Distribution and Intensity of Prehistoric Land Use in Eastern Iberia"
subtitle: "R Markdown Scripts for Reproducing Analyses"
output:
  html_notebook: default
  pdf_document: 
  html_document:
    df_print: paged
---


## Get all the packages ready

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggtext)
library(tidymodels)
library(caret)
library(ranger)
library(themis)
library(vip)
library(readr)
library(rcarbon)
library(Bchron)
library(doParallel)
library(parallel)

ncores <- max(1L, detectCores(), na.rm = TRUE)
options(Ncpus = ncores)
```


## Load data

```{r load Medland data}
# Load Medland survey collection data
medland_survey2014_2017 <- read_csv("data/medland_survey2014_2017.csv")

# Load training data for Random Forest modeling
training_data_E_Iberia <- read_csv("data/training_data_E_Iberia.csv", locale = locale(encoding = "ISO-8859-1"))

# Load C14 data for SPD analysis
C14_SE_Iberia_all <- read_csv("data/C14_S&E_Iberia_all.csv", locale = locale(encoding = "850"))

```


# Figures 1 and 2 generated in GIS software

# Figure 3: Surface Visibility and Artifact Recovery

```{r fig.height=4, fig.width=6}
medland_survey2014_2017 %>% 
  dplyr::filter(area.sqm>0 & total.lithics>0 & !is.na(visibility)) %>% 
  mutate(lithic.density=total.lithics/area.sqm) %>% 
  ggplot(aes(x=lithic.density), xlim=.02) + 
  geom_histogram(binwidth = .001) + 
  scale_y_log10() + 
  scale_x_continuous(limits = c(0,0.01), breaks = c(0,.002, .004, .006, .008)) + 
  labs(title = "Surface Visibility and Artifact Recovery",
       x="lithic artifacts / km^2",
       y='count of patches') + 
  facet_grid(factor(visibility)~study.area) + 
  theme_bw(base_size = 20) + 
  theme(axis.title.x = element_markdown())
```

### ANOVA for Figure 3: all survey areas and each survey area

```{r}
cat("\nCanal de Navarrés survey area\n")
with(medland_survey2014_2017 %>% 
       dplyr::filter(total.lithics>0 & !is.na(visibility) & study.area == "Canal de Navarrés") %>% 
       mutate(lithic.density=total.lithics/area.sqm), 
  aov(lithic.density~visibility)) %>% summary()

cat("\nHoya de Buñol survey area\n")
with(medland_survey2014_2017 %>% 
       dplyr::filter(total.lithics>0 & !is.na(visibility) & study.area == "Hoya de Buñol") %>% 
       mutate(lithic.density=total.lithics/area.sqm), 
  aov(lithic.density~visibility)) %>% summary()

cat("\nCocina/Catadau survey area\n")
with(medland_survey2014_2017 %>% 
       dplyr::filter(total.lithics>0 & !is.na(visibility) & study.area == "Cocina/Catadau") %>% 
       mutate(lithic.density=total.lithics/area.sqm), 
  aov(lithic.density~visibility)) %>% summary()
```


# Random Forest Model for Chronological Unmixing (Figure 4 and Table 4)

## Data preparation

### Prepare data for Valencia dated assemblages

```{r prepare training data}


# Modify training data based on ML and Bayesian testing: 
#  Merge ENEOL and MNEOL
#  Remove undiagnostic lithics for age estimates
#  Sort factor levels chronologically 

training_data_E_Iberia <- training_data_E_Iberia %>% 
  select(-undiag.lithics, -total.lithics, -citation) %>% 
  mutate(period = replace(period, period == "MNEOL", "ENEOL"), 
         period = factor(period,
                         levels = c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")))
```


### Prepare data from survey collections for applying Random Forest model
```{r prepare Medland data}
# filter out assemblages with only undiagnostic lithics
medland_survey2014_2017_lithics <- medland_survey2014_2017 %>% 
  dplyr::filter(undiag.lithics < total.lithics) %>% 
  select(ID, c(13:27))

# make separate table of assemblage ID and provenience
medland_survey2014_2017_info <- medland_survey2014_2017 %>% 
  dplyr::filter(undiag.lithics < total.lithics) %>% 
  mutate(assemblage = paste(study.area, "-", zone, "-", sector, "-", subsector, sep = "")) %>% 
  select(ID, study.area, assemblage)

# calculate lithic density for each collection patch
medland_survey2014_2017_density <- medland_survey2014_2017 %>% 
  dplyr::filter(undiag.lithics < total.lithics) %>% 
  mutate(assemblage = paste(study.area, "-", zone, "-", sector, "-", subsector, sep = ""), 
         density.km2 = total.lithics*1000/area.sqm)%>% 
  select(ID, total.lithics, area.sqm,density.km2)
```

### Split data into training and test sets

```{r split training & test data}
# Partition into training and hold out test / validation sample
set.seed(456) ## if we want to make it completely reproducible
vl.split <- training_data_E_Iberia %>% 
  rsample::initial_split(., prop=.75)

vl.train <- rsample::training(vl.split)
vl.test <- rsample::testing(vl.split)

# save ID data for later analysis
vl.test.id <- vl.test %>% 
  select(ID, period)

```


### create v-fold objects for replicable and comparable cross-validation

10 folds using all the training data
```{r vfold10 all}
set.seed(456)
vf10.all <- vfold_cv(training_data_E_Iberia %>% select(-ID),v=10)
```

10 folds using the 75% training data split
```{r vfold10 train}
set.seed(456)
vf10.train <- vfold_cv(vl.train %>% select(-ID),v=10)
```


## Test Random Forest Model for Estimating Age of Surface Assemblages

### Create and evaluate RF model using a 75% split (vl.split)

#### Define and instantiate a random forest model

```{r RF define model}
valencia.lithics.rf.mod <- 
  rand_forest(trees=500) %>% 
  set_engine("ranger", importance = "impurity") %>% 
  set_mode("classification")

print(valencia.lithics.rf.mod)
```

#### Fit the model to the training data

```{r RF fit model}
valencia.lithics.rf.fit <- 
  valencia.lithics.rf.mod %>% 
  fit(as.factor(period) ~ ., data = vl.train[,2:ncol(vl.train)])

print(valencia.lithics.rf.fit)
```

#### Optional graph: variable importance for random forest model with training set

```{r}
valencia.lithics.rf.fit %>% 
  extract_fit_engine() %>% 
  vip(aesthetics = list(color = "black", fill = "#26ACB5"), num_features = 15) + theme_minimal()
```

#### Extract the fitted data 

```{r RF extract model fit}
valencia.lithics.rf.predicted <- 
  valencia.lithics.rf.fit %>% 
  predict(vl.test) %>% 
  bind_cols(vl.test.id[1:2], ., predict(valencia.lithics.rf.fit, vl.test, type="prob")) %>% 
  rename(predicted.age = .pred_class, 
         MP=.pred_MP,
         UP=.pred_UP,
         EPI=.pred_EPI, 
         MESO=.pred_MESO, 
         ENEOL=.pred_ENEOL, 
         LNEOL=.pred_LNEOL, 
         true.age = period) %>% 
  mutate(true.age = factor(true.age, 
           levels=c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")),
         predicted.age = factor(predicted.age, 
           levels=c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")))

print(valencia.lithics.rf.predicted)
```

#### Figure 4: Graph random forest predictions for test set

```{r RF graph predictions, fig.height=6, fig.width=8}
valencia.lithics.rf.predicted %>% 
  pivot_longer(cols = 4:ncol(valencia.lithics.rf.predicted), 
               names_to = "period", values_to = "probability") %>% 
  mutate(period = factor(period, 
      levels = c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL"))) %>%
  ggplot(aes(x=period, y=probability)) + 
  geom_line(group=1) + 
  geom_vline(aes(xintercept = true.age), color="red", size=2, alpha=.5) +
  geom_vline(aes(xintercept = predicted.age), color="blue", size=0.8) + 
  labs(title="Random Forest Predictions for Each Assemblage", 
       subtitle="black line indicates probability, blue line indicates predicted age, & red line indicates known age",
       x="time period",
       y="probability of predicted time period") + 
  facet_wrap(vars(ID), ncol = 7) + 
  theme_bw(base_size = 20) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        strip.text.x = element_text(size = 14))
```

#### Create confusion matrix for 75% training/test split

```{r RF confusion matrix 1}
library(caret)
with(valencia.lithics.rf.predicted,  
  caret::confusionMatrix(true.age, predicted.age))
```

### Create and evaluate cross-validated random forest model using 10 folds

#### Define and instantiate a random forest model workflow and fit to cross-validated data set

```{r RF xvalidate fit model}
#Create workflow step
valencia.lithics.rf.wf <- 
  workflow() %>% 
  add_model(valencia.lithics.rf.mod) %>% 
  add_formula(period ~ .) #The predictor is contained in add_formula method

set.seed(456) # For reproducibility
valencia.lithics.rf.xv.fit <- 
  valencia.lithics.rf.wf %>% 
  fit_resamples(vf10.all,
                control=control_resamples(save_pred = TRUE))

print(valencia.lithics.rf.xv.fit)
```


#### Collect the cross-validated metrics

```{r RF xvalidate collect metrics}
# Collect the metrics using another model with cross-validation
valencia.lithics.rf.xv.meanpreds <- tune::collect_metrics(valencia.lithics.rf.xv.fit)
print(valencia.lithics.rf.xv.meanpreds)
```


#### Optional Graph: variable importance for cross-validated random forest model

```{r RF VIP all}
valencia.lithics.rf.mod %>% 
  fit(as.factor(period) ~ ., data = training_data_E_Iberia[,2:ncol(training_data_E_Iberia)]) %>% 
  extract_fit_engine() %>% 
  vip(aesthetics = list(color = "black", fill = "#26ACB5"), num_features = 15) + 
  theme_minimal()
```


#### Extract predictions of random forest model with cross-validation

```{r RF xvalidate predictions}
valencia.lithics.rf.xv.predictions <- collect_predictions(valencia.lithics.rf.xv.fit, summarize = TRUE) %>% 
  arrange(.row) %>% 
  rename(predicted.age = .pred_class,
         MP=.pred_MP,
         UP=.pred_UP,
         EPI=.pred_EPI, 
         MESO=.pred_MESO, 
         ENEOL=.pred_ENEOL, 
         LNEOL=.pred_LNEOL,
         true.age=period) %>% 
  select(-.row, -.config) %>% 
  relocate(predicted.age, .after = true.age) %>% 
  bind_cols(training_data_E_Iberia[1], .)

print(valencia.lithics.rf.xv.predictions)
```


#### Optional Graph: results for cross-validated random forest predictions

```{r RF xvalidate graph predictions, fig.height=10, fig.width=14}
valencia.lithics.rf.xv.predictions %>% 
  pivot_longer(cols = 4:ncol(valencia.lithics.rf.xv.predictions), names_to = "period", values_to = "probability") %>% 
  mutate(period = factor(period, levels = c("MP","UP","EPI","MESO","ENEOL","MNEOL","LNEOL"))) %>% 
ggplot(aes(x=period, y=probability)) + 
  geom_line(group=1) + 
  geom_vline(aes(xintercept = true.age), color="red", size=2, alpha=.5) +
  geom_vline(aes(xintercept = predicted.age), color="blue", size=0.8) + 
  labs(title="Random Forest with Cross-Validated Predictions", 
       subtitle="Time Periods for Each Assemblage",
       x="predicted time period\n(blue line indicates prediction & red line indicates radiocarbon age)") +
  facet_wrap(vars(ID)) + 
  theme_bw(base_size = 16) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```


#### Table 4: Confusion matrix for cross-validated random forest predictions

```{r RF xvalidate confusion matrix}
caret::confusionMatrix(
  as.factor(valencia.lithics.rf.xv.predictions$true.age),
  as.factor(valencia.lithics.rf.xv.predictions$predicted.age))
```


## Create random forest model from all training data to estimate ages of surface collections from survey

### Define and instantiate a Random Forest model using all the known training data

```{r Medland RF define model}
medland.survey.rf.mod <- 
  rand_forest(trees=500) %>% 
  set_engine("ranger") %>% 
  set_mode("classification")

print(medland.survey.rf.mod)
```

### Extract fit of Random Forest model

```{r Medland RF fit model}
medland.survey.rf.fit <- 
  medland.survey.rf.mod %>% 
  fit(as.factor(period) ~ ., data = training_data_E_Iberia[,2:ncol(training_data_E_Iberia)])

print(medland.survey.rf.fit)
```

### Apply Random Forest model to surface collections and generate age estimate predictions for each collection

```{r Medland RF extract model fit}
medland.survey.rf.predicted <- 
  medland.survey.rf.fit %>% 
  predict(medland_survey2014_2017_lithics) %>% 
  bind_cols(medland_survey2014_2017_info, ., 
            predict(medland.survey.rf.fit, medland_survey2014_2017_lithics, type="prob")) %>% 
  rename(predicted.period = .pred_class, 
         MP=.pred_MP,
         UP=.pred_UP,
         EPI=.pred_EPI, 
         MESO=.pred_MESO, 
         ENEOL=.pred_ENEOL, 
         LNEOL=.pred_LNEOL) %>% 
  mutate(predicted.period = factor(predicted.period, 
           levels=c("MP", "UP", "EPI", "MESO", "ENEOL", "LNEOL")))

print(medland.survey.rf.predicted)
```

### Calculate age probabilities to assign to collections with only undiagostic lithics
Use the 10th percentile of probabilities from random forest model with dated collections

```{r}
valencia.lithics.rf.probquantiles <- with(valencia.lithics.rf.xv.predictions %>% 
       pivot_longer(cols = 4:ncol(valencia.lithics.rf.xv.predictions), 
                    values_to = "probability"), 
       quantile(probability, probs = c(.1, .25, .5, .75))) 

print(paste("10th percentile of random forest probabilities =", 
            valencia.lithics.rf.probquantiles[1]))

```

### Optional Graph: show quantiles for random forest probabilities

```{r}
# show this in a graph
valencia.lithics.rf.xv.predictions %>% 
  pivot_longer(cols = 4:ncol(valencia.lithics.rf.xv.predictions), names_to = "period", values_to = "probability") %>% 
  mutate(period = factor(period, levels = c("MP","UP","EPI","MESO","ENEOL","MNEOL","LNEOL"))) %>% 
  ggplot(aes(x=probability)) + 
  geom_density() + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[1], color = 'red') + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[2], color = 'green') + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[3], color = 'blue') + 
  geom_vline(xintercept = valencia.lithics.rf.probquantiles[4], color = 'green') +
  labs(title="Distribution of Combined Random Forest Predictions", 
       x="age prediction probabilities \n(blue = median, red = 10th percentile, green = 25th and 75th percentile")

```

### Create files for additional graphing and for output for mapping

```{r Medland RF file for graphing}
# Create base file for graphing and output
medland.survey.rf.graph <-  
  left_join(select(medland_survey2014_2017, ID, study.area, zone, sector, subsector, total.lithics, area.sqm) %>% 
            mutate(assemblage = paste(study.area, "-", zone, "-", sector, "-", subsector, sep = "")), 
            medland.survey.rf.predicted) %>% 
  dplyr::filter(total.lithics>0) %>% 
  mutate(density.km2 = total.lithics/area.sqm/1000)

# Create output file
# Use 10th percentile for overall ubiquity for patches with only undiagnostic lithics
medland.survey.rf.out <- medland.survey.rf.graph %>% 
  mutate(MP = replace_na(MP, valencia.lithics.rf.probquantiles[1]), 
         UP = replace_na(UP, valencia.lithics.rf.probquantiles[1]), 
         EPI = replace_na(EPI, valencia.lithics.rf.probquantiles[1]), 
         MESO = replace_na(MESO, valencia.lithics.rf.probquantiles[1]), 
         ENEOL = replace_na(ENEOL, valencia.lithics.rf.probquantiles[1]), 
         LNEOL = replace_na(LNEOL, valencia.lithics.rf.probquantiles[1])) %>% 
  # occupational ubiquity
  rename(MP_ubiq = MP, 
         UP_ubiq = UP, 
         EPI_ubiq = EPI, 
         MESO_ubiq = MESO,
         ENEOL_ubiq = ENEOL, 
         LNEOL_ubiq = LNEOL) %>% 
  # calculate occupational intensity
  mutate(MP_int = MP_ubiq*density.km2/800, 
         UP_int = UP_ubiq*density.km2/250, 
         EPI_int =  EPI_ubiq*density.km2/40, 
         MESO_int = MESO_ubiq*density.km2/35, 
         ENEOL_int = ENEOL_ubiq*density.km2/25, 
         LNEOL_int = LNEOL_ubiq*density.km2/12)

# create file to graph results of patches with diagnostic lithics
medland.survey.rf.graph <-  medland.survey.rf.graph %>% 
  dplyr::filter(!is.na(predicted.period)) %>% 
  pivot_longer(cols = 10:15, 
               names_to = "period", 
               values_to = "ubiquity") %>% 
   mutate(period = factor(period, 
                         levels = c("MP","UP","EPI","MESO","ENEOL","MNEOL","LNEOL")),
         age = case_when(period == "MP" ~ 80000, 
                         period == "UP" ~ 25000, 
                         period == "EPI" ~ 13000, 
                         period == "MESO" ~ 9000, 
                         period == "ENEOL" ~ 6000,
                         period == "LNEOL" ~ 4000), 
         predicted.age = case_when(
                         predicted.period == "MP" ~ 80000, 
                         predicted.period == "UP" ~ 25000, 
                         predicted.period == "EPI" ~ 13000, 
                         predicted.period == "MESO" ~ 9000, 
                         predicted.period == "ENEOL" ~ 6000,
                         predicted.period == "LNEOL" ~ 4000), 
         duration.centuries = case_when(
                         period == "MP" ~ 800, 
                         period == "UP" ~ 250, 
                         period == "EPI" ~ 40, 
                         period == "MESO" ~ 35, 
                         period == "ENEOL" ~ 25,
                         period == "LNEOL" ~ 12))
```

### Optional Graph: occupational ubiquity for all survey patches with diagnostic lithics
                         
```{r Medland RF graph ubiquity predictions, fig.height=12, fig.width=10, warning=FALSE}
medland.survey.rf.graph %>% 
  ggplot(aes(x=period, y=ubiquity)) + 
  geom_rect(aes(fill = study.area),xmin = -Inf,xmax = Inf, ymin = -Inf,ymax = Inf,alpha = 0.1) +
  geom_line(group=1) + 
  #geom_vline(aes(xintercept = predicted.period), color="blue") + 
  labs(title="Random Forest Age Estmates for Medland Survey Data", 
       subtitle="Time Periods for Each Assemblage, Colored by Study Area",
       x="predicted time period\n(blue line indicates maximum predicted probability)") +
  facet_wrap(~ assemblage + ID) + 
  theme_bw(base_size = 16) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```

### Optional Graph: land use intensity for all survey patches with diagnostic lithics
                         
```{r Medland RF graph intensity predictions, fig.height=12, fig.width=10, warning=FALSE}
medland.survey.rf.graph %>% 
  ggplot(aes(x=period, y=ubiquity*density.km2/duration.centuries)) + 
  geom_rect(aes(fill = study.area),xmin = -Inf,xmax = Inf, ymin = -Inf,ymax = Inf,alpha = 0.1) +
  geom_line(group=1) + 
  #geom_vline(aes(xintercept = predicted.period), color="blue") + 
  labs(title="Land Use Intensity Estmates for Medland Survey Data", 
       subtitle="Time Periods for Each Assemblage, Colored by Study Area",
       x="predicted time period\n(blue line indicates maximum predicted probability)",
       y="estimated artifact accumulation rate\n(artifacts/km2/century)") +
  facet_wrap(~ assemblage + ID) + 
  theme_bw(base_size = 16) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```

### Figure 6a

```{r Figure: Medland RF graph ubiquity, fig.height=3, fig.width=6, warning=FALSE}
medland.survey.rf.graph %>% 
  dplyr::filter(ID==238 | ID==960) %>% 
ggplot(aes(x=period, y=ubiquity)) + 
  geom_line(group=1, size=2) + 
  labs(title="Occupational Ubiquity for 2 Survey Patches", 
       subtitle="Random Forest Age Estmates",
       x="predicted time period", 
       y="occupational ubiquity") +
  facet_wrap(~ assemblage) + 
  theme_bw(base_size = 20) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```

### Figure 6b

```{r Figure: Medland RF graph intensity, fig.height=3, fig.width=6, warning=FALSE}
medland.survey.rf.graph %>% 
  dplyr::filter(ID==238 | ID==960) %>% 
ggplot(aes(x=period, y=ubiquity*density.km2/duration.centuries)) + 
  geom_line(group=1, size=2) + 
  labs(title="Land Use Intensity for 2 Survey Patches", 
       subtitle="Random Forest Age Estmates",
       x="predicted time period", 
       y="artifacts / km^2 /century") +
  facet_wrap(~ assemblage) + 
  theme_bw(base_size = 20) + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        axis.title.y = element_markdown()) 

```


#### Save random forest dating file to csv for mappoing in GIS

```{r Medland RF output}
write_csv(medland.survey.rf.out, "medland_survey_rf.csv")
```


## Aggregate summaries of ubiquity and intensity for each valley

### Calcuate area surveyed totals for each valley
```{r}
totals.surveyed <- medland_survey2014_2017 %>% 
  select(study.area, area.sqm) %>% 
  group_by(study.area) %>% 
  summarise(total.area.sqm=sum(area.sqm))
```

### Figure 10a: aggregate ubiquity for each valley
```{r fig.height=4, fig.width=3}
left_join(medland.survey.rf.graph, totals.surveyed) %>% 
  dplyr::filter(ubiquity > quantile(medland.survey.rf.graph$ubiquity, 
                                     probs = .5)) %>% 
  group_by(study.area, period) %>% 
  summarize(area.sum = sum(area.sqm), 
            total.area.sqm = first(total.area.sqm)) %>% 
  select(study.area, period, area.sum, total.area.sqm) %>% 
  mutate(pct.coverage = area.sum/total.area.sqm) %>% 
  ungroup()  %>% 
  add_row(study.area = "Canal de Navarrés",
          period = "EPI",
          area.sum = 0,
          total.area.sqm = 4107582,
          pct.coverage = 0) %>%
  add_row(study.area = "Cocina/Catadau",
          period = "EPI",
          area.sum = 0,
          total.area.sqm = 1095683,
          pct.coverage = 0) %>%
  add_row(study.area = "Cocina/Catadau",
          period = "MESO",
          area.sum = 0,
          total.area.sqm = 1095683,
          pct.coverage = 0) %>%
  add_row(study.area = "Hoya de Buñol",
          period = "EPI",
          area.sum = 0,
          total.area.sqm = 1551377,
          pct.coverage = 0) %>% 

# Needed for optional upper quartile graphing   
  # add_row(study.area = "Canal de Navarrés",
  #         period = "MP",
  #         area.sum = 0,
  #         total.area.sqm = 4107582,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Canal de Navarrés",
  #         period = "MESO",
  #         area.sum = 0,
  #         total.area.sqm = 4107582,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Canal de Navarrés",
  #         period = "LNEOL",
  #         area.sum = 0,
  #         total.area.sqm = 4107582,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Cocina/Catadau",
  #         period = "MP",
  #         area.sum = 0,
  #         total.area.sqm = 1095683,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Cocina/Catadau",
  #         period = "LNEOL",
  #         area.sum = 0,
  #         total.area.sqm = 1095683,
  #         pct.coverage = 0) %>%
  # add_row(study.area = "Hoya de Buñol",
  #         period = "MP",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
  # add_row(study.area = "Hoya de Buñol",
  #         period = "MESO",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
  # add_row(study.area = "Hoya de Buñol",
  #         period = "LNEOL",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
  
    
  ggplot(aes(x=factor(period, levels = 
                  c('MP','UP','EPI','MESO','ENEOL','LNEOL')),
             y=pct.coverage, group=1)) + 
  geom_line(lwd=1.5) + 
  facet_wrap(~study.area, ncol = 1) + 
  labs(title='Aggregate Occupational Ubiquity', 
       subtitle='Proportion of study area surveyed\nwith ubiquity above the median', 
       x='period', 
       y='proportion') + 
  theme_bw(base_size = 16)
```

### Figure 10b: aggregate intensity for each valley

```{r fig.height=4, fig.width=3}
left_join(medland.survey.rf.graph, totals.surveyed) %>% 
  mutate(intensity = ubiquity*density.km2/duration.centuries) %>% 
  dplyr::filter(intensity >= quantile(medland.survey.rf.graph$ubiquity*medland.survey.rf.graph$density.km2/medland.survey.rf.graph$duration.centuries, probs =.5)) %>% 
  group_by(study.area, period) %>% 
  summarize(area.sum = sum(area.sqm), 
            total.area.sqm = first(total.area.sqm), 
            density.km2 = first(density.km2)) %>% 
  select(study.area, period, area.sum, total.area.sqm) %>% 
  mutate(pct.coverage = area.sum/total.area.sqm) %>% 
  ungroup %>%
  add_row(study.area = "Canal de Navarrés",
          period = "MP",
          area.sum = 0,
          total.area.sqm = 4107582,
          pct.coverage = 0) %>% 
  add_row(study.area = "Hoya de Buñol",
          period = "MP",
          area.sum = 0,
          total.area.sqm = 1551377,
          pct.coverage = 0) %>% 
    
# Needed for optional upper quartile graphing
  # add_row(study.area = "Hoya de Buñol",
  #         period = "UP",
  #         area.sum = 0,
  #         total.area.sqm = 1551377,
  #         pct.coverage = 0) %>% 
      
  ggplot(aes(x=factor(period, levels = 
                  c('MP','UP','EPI','MESO','ENEOL','LNEOL')),
             y=pct.coverage, group=1)) + 
  geom_line(lwd=1.5) + 
  facet_wrap(~study.area, ncol = 1) + 
  labs(title='Aggregate Land Use Intensity', 
       subtitle='Proportion of study area surveyed\nwith intensity above the median', 
       x='period', 
       y='proportion') + 
  theme_bw(base_size = 16)
```


# Figure 11: SPD Analyses of Prehistoric Demography

## Prepare Data
Only use dates with COV ≤ 0.05
```{r}
C14_SE_Iberia_all <- C14_SE_Iberia_all %>% dplyr::filter(C14.CV<0.05 & C14.SD>0)
```

## Calibrate Dates with BChron

```{r calibrate}
all.dates.calibrated <- with(C14_SE_Iberia_all, BchronCalibrate(ages = C14.mean, ageSds = C14.SD, calCurves = calib.curve, positions = site))

C14_SE_Iberia_all$BP.cal.median <- sapply(1:length(all.dates.calibrated), function(x) round(median(all.dates.calibrated[[x]]$ageGrid)))

```

## Bin Dates
```{r}
C14_SE_Iberia_all.bins <- C14_SE_Iberia_all %>% 
  with(., binPrep(site, C14.mean, 100))
```

## Model Test
```{r}
C14_SE_Iberia_all.modeltest <- C14_SE_Iberia_all %>% 
  with(., calibrate(x=C14.mean, errors=C14.SD, calCurves = calib.curve, normalised=TRUE, calMatrix=FALSE)) %>% 
  modelTest(., 
            errors = C14_SE_Iberia_all$C14.SD, 
            timeRange = c(35000,3000), 
            runm = 500, 
            model="exponential", 
            datenormalised=TRUE, 
            nsim = 200, 
            ncores = ncores,
            method = 'calsample', 
            bins = C14_SE_Iberia_all.bins)
```

## Plot SPD
```{r fig.width=9, fig.height=4}
par(mar=c(7,7,7,3))
plot(C14_SE_Iberia_all.modeltest, xlim = c(30000,4000), col.obs = 'black', lwd.obs = 5, drawaxes = F)
axis(1, cex.axis = 2, pos = -.01, at=(seq(30000,0, by=-5000)))
axis(2, cex.axis = 2, pos = 30200)
mtext(side=1, line=5, "calibrated years BP", cex=2.5)
mtext(side=2, line=4, "summed probability density", cex=2.5)
title(main=paste("Southern and Eastern Iberia SPD (N = ", nrow(C14_SE_Iberia_all), ")\n"), cex.main = 3)
```


